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> I’m more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and

> I’m more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The English term for Neijuan is “Involution” from the book Agricultural Involution. Agricultural involution describes the intensification of farming that raises productivity per square meter while leaving productivity per head unchanged. This resonates

veqq 22d ago
Also known has the Red Queen's Race
MangoCoffee 22d ago
it reminds me of a thread I read on PTT, Taiwan's Reddit. AI finally achieved what humans could not. Managers must give exact context for what they want, must pay exact wages (tokens), and can't delay salary payments (which seems to be a problem in China).
- djmips 22d ago
  Yeah it's a funny thing - a lot of the things you need to feed the model are things that actually would have helped humans...
  - TeMPOraL 22d ago
    Starting with agentic task-time "grounding" being just good documentation, and "skills" being just playbooks and user guides. Hell, skills are increasingly paired with dedicated CLI tools, that remove jank from actual utilities and adapts them to be token efficient. So now, any CLI `tool` people want AI to use eventually grows `tool/SKILL.md` and then a `tool-for-llms` wrapper that exposes task-specific, logical, higher level interface, then the skill is rewritten in terms of "for LLMs" wrapper. The procedural knowledge moves from Markdown into the wrapper, making the skill more token efficient, and both skill and the tools are optimized for common tasks and... at this point, we are doing actual UX engineering. Now the truly interesting part is the difference between what's good UX/DX for LLMs vs humans. Turns out, the conceptual/abstract/cognitive part is pretty much the same: which is why skills still look indistinguishable from well-written documentation for humans, and why the commands exposed by "tool but for LLMs" make sense to us. Same way of grouping ideas into higher level concepts. No, the main difference is just that LLMs are perfectly content with tightly packed unprettified JSON, or other forms of Perl line noise. The tool output doesn't need to look nice, or to have any spatial structure - they're reading it token by token anyway, and the tokens come from a tokenizer that's reading it byte by byte. That points at an interesting asymmetry for humans. LLMs are doing I/O the same way in both directions: sequences in, sequences out. Humans only do sequential output - inputs, particularly visual, are processed holistically. For us, what's easy to read is hard to write, and what's easy to write is hard to read. LLMs don't have this friction. (I don't know what the implications of this are, I just find this interesting.)
- UncleMeat 22d ago
  I never quite realized this until just reading this and now it has come into sharp focus. Incredible. I've spent years trying to convince my director to have our org invest in documentation and monitoring to no avail. Now he is telling us to spend dedicated time on monitoring and documentation so that agents can better diagnose and fix bugs. He is doing this because his boss is mad that our org isn't "agentic" enough. Except... because we underinvested in the past we have a bunch of services where the institutional knowledge is gone and people are having AI write the documentation...
hatmatrix 22d ago
But doesn't this reduce the required amount of farmland?
- colordrops 22d ago
  Yes, the extra farmland gets taken by the AI companies.
- layer8 22d ago
  There is no “required” amount. More gets produced using the same area, using more people, but keeping these people in poverty because productivity per capita doesn’t increase. Instead the gains from the increased volume of production get captured by an elite. See https://link.springer.com/article/10.1186/s41257-019-0021-y https://link.springer.com/article/10.1186/s41257-019-0021-y.
  - omnicognate 22d ago
    What corresponds to land in the AI analogy to this?
    - layer8 22d ago
      There is no direct analogy, the term has drifted since. See https://en.wikipedia.org/wiki/Neijuan https://en.wikipedia.org/wiki/Neijuan.
squidbeak 22d ago
This sounds like all engineering, rather than just AI. The greatest effort given to the last small difficult details, often for results that seem trifling but which matter at scale. But it's a poor argument. The code improvements with these things is hardly marginal - Opus 4 was only 16 months ago. How many of the grumblers would want to ditch their modern stalwarts and return to it? What is marginal is the nitpicking - and like anything in tighter bounds, it's more intense with a narrower scope. These threads always have many dissatisfied voices with repeating complaints - about overwrought thinking and disappointing output - alongside others who are amazed at the sudden real extra capabilities. Both are true at once - capabilities are rapidly increasing, but nowhere near ideal, which is why this attempt to tag it as Neijuan, though interesting, is ultimately a load of bollocks.
- littlexsparkee 15d ago
  Little late here but it matters if they're ascribing it to the technology vs its application to people - squeezing more out of people for relatively little gain hence modern burnout. I believe they were describing the latter.
foxglacier 22d ago
It surely resonates if your reason for working is to extract wealth from others (ie. be a parasite) but if it's to improve things for everyone, then it's wonderful. You might still work just as much and get paid just as much but produce a lot more because of technology.

veqq 22d ago

Also known has the Red Queen's Race

MangoCoffee 22d ago

it reminds me of a thread I read on PTT, Taiwan's Reddit. AI finally achieved what humans could not. Managers must give exact context for what they want, must pay exact wages (tokens), and can't delay salary payments (which seems to be a problem in China).

djmips 22d ago
Yeah it's a funny thing - a lot of the things you need to feed the model are things that actually would have helped humans...
- TeMPOraL 22d ago
  Starting with agentic task-time "grounding" being just good documentation, and "skills" being just playbooks and user guides. Hell, skills are increasingly paired with dedicated CLI tools, that remove jank from actual utilities and adapts them to be token efficient. So now, any CLI `tool` people want AI to use eventually grows `tool/SKILL.md` and then a `tool-for-llms` wrapper that exposes task-specific, logical, higher level interface, then the skill is rewritten in terms of "for LLMs" wrapper. The procedural knowledge moves from Markdown into the wrapper, making the skill more token efficient, and both skill and the tools are optimized for common tasks and... at this point, we are doing actual UX engineering. Now the truly interesting part is the difference between what's good UX/DX for LLMs vs humans. Turns out, the conceptual/abstract/cognitive part is pretty much the same: which is why skills still look indistinguishable from well-written documentation for humans, and why the commands exposed by "tool but for LLMs" make sense to us. Same way of grouping ideas into higher level concepts. No, the main difference is just that LLMs are perfectly content with tightly packed unprettified JSON, or other forms of Perl line noise. The tool output doesn't need to look nice, or to have any spatial structure - they're reading it token by token anyway, and the tokens come from a tokenizer that's reading it byte by byte. That points at an interesting asymmetry for humans. LLMs are doing I/O the same way in both directions: sequences in, sequences out. Humans only do sequential output - inputs, particularly visual, are processed holistically. For us, what's easy to read is hard to write, and what's easy to write is hard to read. LLMs don't have this friction. (I don't know what the implications of this are, I just find this interesting.)
UncleMeat 22d ago
I never quite realized this until just reading this and now it has come into sharp focus. Incredible. I've spent years trying to convince my director to have our org invest in documentation and monitoring to no avail. Now he is telling us to spend dedicated time on monitoring and documentation so that agents can better diagnose and fix bugs. He is doing this because his boss is mad that our org isn't "agentic" enough. Except... because we underinvested in the past we have a bunch of services where the institutional knowledge is gone and people are having AI write the documentation...

hatmatrix 22d ago

But doesn't this reduce the required amount of farmland?

colordrops 22d ago
Yes, the extra farmland gets taken by the AI companies.
layer8 22d ago
There is no “required” amount. More gets produced using the same area, using more people, but keeping these people in poverty because productivity per capita doesn’t increase. Instead the gains from the increased volume of production get captured by an elite. See https://link.springer.com/article/10.1186/s41257-019-0021-y https://link.springer.com/article/10.1186/s41257-019-0021-y.
- omnicognate 22d ago
  What corresponds to land in the AI analogy to this?
  - layer8 22d ago
    There is no direct analogy, the term has drifted since. See https://en.wikipedia.org/wiki/Neijuan https://en.wikipedia.org/wiki/Neijuan.

squidbeak 22d ago

This sounds like all engineering, rather than just AI. The greatest effort given to the last small difficult details, often for results that seem trifling but which matter at scale. But it's a poor argument. The code improvements with these things is hardly marginal - Opus 4 was only 16 months ago. How many of the grumblers would want to ditch their modern stalwarts and return to it? What is marginal is the nitpicking - and like anything in tighter bounds, it's more intense with a narrower scope. These threads always have many dissatisfied voices with repeating complaints - about overwrought thinking and disappointing output - alongside others who are amazed at the sudden real extra capabilities. Both are true at once - capabilities are rapidly increasing, but nowhere near ideal, which is why this attempt to tag it as Neijuan, though interesting, is ultimately a load of bollocks.

littlexsparkee 15d ago
Little late here but it matters if they're ascribing it to the technology vs its application to people - squeezing more out of people for relatively little gain hence modern burnout. I believe they were describing the latter.

foxglacier 22d ago

It surely resonates if your reason for working is to extract wealth from others (ie. be a parasite) but if it's to improve things for everyone, then it's wonderful. You might still work just as much and get paid just as much but produce a lot more because of technology.